Towards explainable fuzzy AI : concepts, paradigms,tools, and techniques / Vladik Kreinovich.
Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot te...
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| Online Access: |
Full Text (via Springer) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2022.
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| Series: | Studies in computational intelligence ;
v. 1047. |
| Subjects: |
| Summary: | Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI. |
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| Physical Description: | 1 online resource. |
| ISBN: | 9783031099748 3031099745 |
| Source of Description, Etc. Note: | Print version record. |